Concept of a Multi-agent Based Decentralized Production System for the Automotive Industry
To face the challenges of today’s market requirements, a huge effort is made to plan continuous flow manufacturing systems used today. Simultaneously disturbances during the production have decisive negative effects on the effectiveness. To mitigate this problem, current research programs try to use flexible production systems with a high degree of self-organization. In this paper a novel concept for a flexible decentralized production system is described which combines the planning method of a precedence graph and a multi-agent-system that forms a modular control system. Furthermore first results are presented that have been achieved by a pilot demonstrator and simulation experiments.
KeywordsCyber-physical systems AGV routing Path planning Task assignment Production planning Decentralized production systems Agent ontology Industry 4.0 Intra-logistic simulation
This work was supported by the German Federal Ministry for Economic Affairs and Energy (BMWi) under the “AUTONOMIK fuer Industrie 4.0” research program within the project SMART FACE (Grant no. 01MA13007). The project consortium consists of industrial companies and research institutions, namely Logata Digital Solutions, F/L/S Fuzzy Logik Systeme, Lanfer Automation, Continental AG, SICK AG, Volkswagen AG, TU Dortmund University, and Fraunhofer IML.
- 2.van den Berg, J., Lin, M., Manocha, D.: Reciprocal velocity obstacles for real-time multi-agent navigation. In: IEEE International Conference on Robotics and Automation, ICRA 2008, pp. 1928–1935, May 2008Google Scholar
- 3.Branisso, L.B., Kato, E.R.R., Pedrino, E.C., Morandin, O., Tsunaki, R.H.: A multi-agent system using fuzzy logic to increase agv fleet performance in warehouses. In: 2013 III Brazilian Symposium on Computing Systems Engineering, pp. 137–142 (2013)Google Scholar
- 4.Frey, D., Woelk, P.O., Stockheim, T., Zimmermann, R.: Integrated multi-agent-based supply chain management. In: WET ICE 2003. Proceedings. Twelfth IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises, pp. 24–29, June 2003Google Scholar
- 5.Giglio, D.: Task scheduling for multiple forklift AGVs in distribution warehouses. In: Proceedings of the 2014 IEEE Emerging Technology and Factory Automation (ETFA), pp. 1–6, September 2014Google Scholar
- 6.Kamagaew, A., Stenzel, J., Nettstraeter, A., ten Hompel, M.: Concept of cellular transport systems in facility logistics. In: 2011 5th International Conference on Automation, Robotics and Applications (ICARA), pp. 40–45, December 2011Google Scholar
- 7.Koebler, J.: Paula on tour (2017). https://audi-illustrated.com/en/audi-encounter-01-2017/paula-on-tour
- 8.ter Mors, A.: Conflict-free route planning in dynamic environments. In: International Conference on Intelligent Robots and Systems 2011, pp. 2166–2171 (2011)Google Scholar
- 9.Regele, R., Levi, P.: Cooperative multi-robot path planning by heuristic priority adjustment. In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5954–5959, October 2006Google Scholar
- 11.Schwarz, C., Schachmanow, J., Sauer, J., Overmeyer, L., Ullmann, G.: Self guided vehicle systems. Logistics J. 2013(12) (2013)Google Scholar
- 13.Yu, M., Zhang, W., Klemm, P.: Multi-agent based reconfigurable manufacturing execution system. In: 2007 IEEE International Conference on Industrial Engineering and Engineering Management, pp. 718–722, December 2007Google Scholar